class_docstr: The DDPOTrainer uses Deep Diffusion Policy Optimization to optimise diffusion models.
  Note, this trainer is heavily inspired by the work here: https://github.com/kvablack/ddpo-pytorch
  As of now only Stable Diffusion based pipelines are supported
  
  Attributes:
      **config** (`DDPOConfig`) -- Configuration object for DDPOTrainer. Check the documentation of `PPOConfig` for more
       details.
      **reward_function** (Callable[[torch.Tensor, Tuple[str], Tuple[Any]], torch.Tensor]) -- Reward function to be used
      **prompt_function** (Callable[[], Tuple[str, Any]]) -- Function to generate prompts to guide model
      **sd_pipeline** (`DDPOStableDiffusionPipeline`) -- Stable Diffusion pipeline to be used for training.
      **image_samples_hook** (Optional[Callable[[Any, Any, Any], Any]]) -- Hook to be called to log images
docstr: Train the model for a given number of epochs
docstr_tok: ["Train","the","model","for","a","given","number","of","epochs"]
code: def train(self, epochs: Optional[int] = None):
      """
      Train the model for a given number of epochs
      """
      global_step = 0
      if epochs is None:
          epochs = self.config.num_epochs
      for epoch in range(self.first_epoch, epochs):
          global_step = self.step(epoch, global_step)
  
imports: import os
  import warnings
  from collections import defaultdict
  from concurrent import futures
  from typing import Any, Callable, Optional, Tuple
  from warnings import warn
  import torch
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from huggingface_hub import whoami
  from ..models import DDPOStableDiffusionPipeline
  from .None import BaseTrainer, DDPOConfig
  from .utils import PerPromptStatTracker
label_desc: Use image node_id 12 for calling the DDPOTrainer obj's underlying member method code with example usage: obj.train(epochs) without return types